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Orthogonal methods based ant colony search for solving continuous optimization problems

Authors
Hu, Xiao-MinZhang, JunLi, Yun
Issue Date
Jan-2008
Publisher
Zhongguo Kexueyan Ganguang Huaxue Yanjiusuo
Keywords
ant algorithm; function optimization; orthogonal design
Citation
Journal of Computer Science and Technology, v.23, no.1, pp 2 - 18
Pages
17
Indexed
SCIE
SCOPUS
Journal Title
Journal of Computer Science and Technology
Volume
23
Number
1
Start Page
2
End Page
18
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115968
DOI
10.1007/s11390-008-9111-5
ISSN
1000-9000
1860-4749
Abstract
Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization - API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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